Environment-Aware Adaptive Transmission for Adaptive Video Streaming Based on Edge Computing in High-speed rail Scenarios

As High-speed rail becomes a popular way to travel, users have a high demand for streaming services. In High-speed rail scenarios, users move fast and base stations handover frequently. Most of the existing network bandwidth prediction algorithms and bitrate selection algorithms are proposed based on low-speed scenarios. These algorithms are difficult to adapt to high-speed mobile scenarios. To solve this problem, this paper proposes an adaptive streaming media transmission method using edge computing, High-speed rail status and cross-layer information (EHCI) in the 5G network environment. Firstly, a QoE model and a coordinated transmission architecture using edge computing, High-speed rail operation status and cross-layer information are proposed. Secondly, a media transcoding algorithm and rate selection algorithm are proposed. Finally, the simulation experiment is carried out in this paper. Simulation results demonstrate that the method proposed in this paper can well improve the QoE of High-speed rail passengers, and is helpful to the study of the optimized transmission of streaming media in High-speed rail scenarios.